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Chater, N. and Oaksford, Michael (2017) Theories or fragments? Behavioral
and Brain Sciences 40 (e253), pp. 30-31. ISSN 0140-525X.
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BBS COMMENTARY
ABSTRACT 65 words MAIN TEXT 813 words REFERENCES
ENTIRE TEXT 241 words 1119 words
Theories or fragments?
Nick Chatera1 and Mike Oaksforda2
a1 Behavioural Science Group, Warwick Business School, University of Warwick
Coventry, CV4 7AL, UK +44 (0)24 7652 4506
http://www.wbs.ac.uk/about/person/nick-chater/
a2 Room 531, Department of Psychological Sciences,
Birkbeck, University of London, Malet Street, London WC1E 7HX +44 (0)20 7079 0879
ABSTRACT:
Lake et al argue persuasively that modelling human-like intelligence requires flexible,
compositional representations in order to embody world knowledge. But human
knowledge is too sparse and self-contradictory to be embedded in “intuitive theories.”
We argue instead that knowledge is grounded in exemplar-based learning and
generalization, combined with high flexible generalization, a viewpoint compatible
both with non-parametric Bayesian modelling and sub-symbolic methods such as
MAIN TEXT:
Lake et al make a powerful case for the modelling human-like intelligence depends on
highly flexible, compositional representations, to embody world knowledge. But will
such knowledge really be embedded in “intuitive theories” of physics or psychology?
This commentary argues that there is a paradox at the heart of the “intuitive theory”
view point---that has be-devilled analytic philosophy and symbolic artificial
intelligence: human knowledge is both (i) extremely sparse and (ii) self-contradictory
(e.g., Oaksford & Chater 1991).
The sparseness of intuitive knowledge is exemplified in Rozenbilt and Keil’s
(2002) discussion of the “illusion of explanatory depth.” We have the feeling that we
understand how a crossbow works, how a fridge stays cold, or how electricity flows
around the house. Yet, when pressed, few of us can provide much more than sketchy
and incoherent fragments of explanation. Thus, our causal models of the physical
world appear shallow. The sparseness of intuitive psychology seems at least as
striking: indeed, our explanations of our own and other’s behavior often appear to be
highly ad hoc (Nisbett & Ross 1980).
Moreover, our physical and psychological intuitions are also
self-contradictory. The foundations of physics and rational choice theory has consistently
shown how remarkably few axioms (e.g., the laws of thermodynamics; the axioms of
decision theory) completely fix a considerable body of theory. Yet our intuitions
about heat and work, or probability and utility, are vastly richer and more
amorphous—and cannot be captured in any consistent system (e.g., some of our
intuitions may imply our axioms; but others will contradict them). Indeed,
assumptions (as illustrated by Russell’s paradox, which unexpectedly exposed a
contradiction in Frege’s attempted logical foundation for mathematics, Irvine &
Deutsch 2016).
The sparse and contradictory nature of our intuitions explains why explicit
theorizing requires continually ironing out contradictions, making vague concepts
precise, and radically distorting or replacing existing concepts. And the lesson of two
and half millennia of philosophy is arguable that clarifying even the most basic
concepts, such as ‘object’ or ‘the good’ can be entirely intractable, a lesson re-learned
in symbolic AI. In any case, the raw materials for this endeavor—our disparate
intuitions—may not properly be viewed as organized as theories at all.
If this is so, how do we interact so successfully in the physical and social
worlds? We have experience, whether direct or by observation or instruction, of
crossbows, fridges and electricity, to be able to interact with them in familiar ways.
Indeed, our ability to make sense of new physical situations often appears to involve
creative extrapolation from familiar examples: e.g., assuming that heavy objects will
fall faster than light objects, even in a vacuum, or where air resistance can be
neglected. Similarly, we have a vast repertoire of experience of human interaction,
from which we can generalize to new interactions. Generalization from such
experiences, to deal with new cases, can be extremely flexible and abstract
(Hofstadter 2001). For example, the perceptual system uses astonishing ingenuity to
construct complex percepts (e.g., human faces) from highly impoverished signals
(e.g., Hoffman 2000; Rock 1983) or interpret art (Gombrich 1960).
We suspect that the growth and operation of cognition is more closely
analogous to case law than it is to scientific theory. Each new case is decided by
precedents from past cases; and the history of cases creates an intellectual tradition
which is only locally coherent, often ill-defined, but surprisingly effective in dealing
with a complex and ever-changing world. In short, knowledge has the form of a
loosely inter-linked history of reusable fragments, each building on the last, rather
than being organized into anything resembling a scientific theory.
Recent work on construction-based approaches to language exemplify this
viewpoint in the context of linguistics (e.g., Goldberg 1995). Rather than seeing
language as generated by a theory (a formally specified grammar) and the acquisition
of language as the fine-tuning of that theory, such approaches see language as a
tradition, where each new language processing episode, like a new legal case, is dealt
with by reference to past instances (Christiansen & Chater 2016). In both law and
language (see Blackburn 1984), there will be a tendency to impose local coherence
across similar instances, but there will typically be no globally coherent theory from
which all cases can be generated.
Case-, instance- or exemplar-based theorizing has been widespread in the
cognitive sciences (e.g., Kolodner 1993; Logan 1988; Medin & Shaffer 1978).
Exploring how creative extensions of past experience can be used to deal with new
experience (presumably by processes of analogy and metaphor rather than deductive
theorizing from basic principles) provides an exciting challenge for artificial
intelligence, whether from a non-parametric Bayesian standpoint or a neural network
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Rozenblit, L., & Keil, F. (2002). The misunderstood limits of folk science: An illusion of
